The invention will be described primarily in the context of Orthogonal Frequency Division Multiplexing (OFDM) which is the dominant multiplexing and modulation technique used in wireless communications. OFDM transmits multiple data streams by assigning each of them uniquely to one or more of a large number of sub-carriers where each sub-carrier operates at a unique carrier frequency (tone). The adjacent sub-carrier frequencies or tones have a fixed frequency difference between them. The data is carried in each sub-carrier by modulating its amplitude or phase, or both. In practice many of the sub-carriers are not used, typically the unused sub-carriers include a small number at the centre of the transmission band, and a block at both edges of the transmission band.
At the transmitter (TX), OFDM enables the efficient use of the available channel bandwidth and the easy control of the signal spectrum mask.
At the Receiver (RX), OFDM allows simple equalization and is robust to constant timing offset due to the adoption of a Cyclic Prefix (CP) in the transmission. However, to optimize the system performance, the receiver is often required to estimate the signal to noise ratio (SNR). For instance, knowledge of the SNR is useful for clear channel assessment, soft-decision channel decoding, Transmitter power control, adaptive coding and modulation, bit loading and hand-off.
SNR estimation has been investigated, especially in the context of single carrier (SC) modulation schemes [1-4]. Some of the SC SNR estimation schemes can be directly adapted to OFDM modulation [5], and these can be classified into two broad categories:                data-aided (DA), and        non-data-aided (NDA), which are also called ‘blind’ schemes.        
Data-aided schemes require some known data to be transmitted, for example, in some preselected pilot subcarriers in the Payload field. Alternatively, one or more training OFDM symbols can be transmitted in the Preamble or the Channel Estimation fields. OFDM SNR estimation schemes can further be divided into time-domain (TD) and frequency-domain (FD) processing algorithms.
References [6-9] describe known blind OFDM SNR estimation algorithms that can be applied to the Payload field without the requirement of preselecting the pilot subcarriers. The time domain signal during the Cyclic Prefix (CP) interval and towards the end of the OFDM symbol are highly correlated. In [6, 7], this correlation is used for SNR estimation. The disadvantage of this approach is that the number of useable samples in one OFDM symbol is very small due to the fact that most of the CP interval is interfered by the previous OFDM symbol. As a result a large number of OFDM symbols are needed to achieve accurate estimation.
By assuming that the signal and noise covariance matrices are different and known, a Maximum-Likelihood (ML) method with high computational complexity is proposed in [8].
The expectation maximization (EM) algorithm is used in [9]. However, the algorithm assumes knowledge of the channel and is therefore dependent on channel estimation accuracy.